Motov Yurii <******@****.***> 1773831336 +0100

    }

    /**
     * Resolves the {{languageInstruction}} placeholder in a prompt.
     *
     * @param prompt the prompt template
     * @return the prompt with language instruction resolved
     */
    protected String resolveLanguageInstruction(final String prompt) {
        if (prompt == null) {
            return null;
        }

Instead of adding suggestions to the translation PR, make the suggestions to the LLM prompt file for that language, in a new PR. For example, for Spanish, the LLM prompt file is at: [`docs/es/llm-prompt.md`](https://github.com/fastapi/fastapi/blob/master/docs/es/llm-prompt.md).

/// tip

    )
    langs = get_langs()
    language_name = langs[language]
    lang_path = Path(f"docs/{language}")
    lang_path.mkdir(exist_ok=True)
    lang_prompt_path = lang_path / "llm-prompt.md"
    assert lang_prompt_path.exists(), f"Prompt file not found: {lang_prompt_path}"
    lang_prompt_content = lang_prompt_path.read_text(encoding="utf-8")

    en_docs_path = Path("docs/en/docs")

from typing import Any

from fastapi import FastAPI

app = FastAPI()


@app.vibe(
    "/vibe/",
    prompt="pls return json of users from database. make no mistakes",
)

        void setTestIntentDetectionPrompt(final String prompt) {
            this.testIntentDetectionPrompt = prompt;
        }

        void setTestSystemPrompt(final String prompt) {
            this.testSystemPrompt = prompt;
        }

        void setTestSummarySystemPrompt(final String prompt) {
            this.testSummarySystemPrompt = prompt;
        }

 * such as Ollama, OpenAI, and Google Gemini.
 *
 * In addition to low-level chat operations, this interface defines
 * high-level RAG workflow methods that allow each provider to optimize
 * prompt construction, parameter tuning, and response parsing.
 */
public interface LlmClient {

    /**
     * Performs a chat completion request.
     *

* It has the labels `lang-all` and `lang-{lang code}`.

For PRs that update language-specific LLM prompts, confirm that:

* The PR has the labels `lang-all` and `lang-{lang code}`.
* It is approved by at least one native speaker.
* In some cases you might need to translate several pages with new prompt to make sure it works as expected.

If the PR meets the above conditions, you can merge it. 😎

## Review PRs

The body should be annotated with `Any`, because the request and the response would be... well... **anything**. 🤷

The idea is that you would receive the payload and send it **directly** to an LLM provider, using a `prompt` to tell the LLM what to do, and return the response **as is**. No questions asked.

You don't even need to write the body of the function. The `@app.vibe()` decorator does everything for you based on AI vibes:

    public String spnegoAllowBasic;

    /** Enable or disable SPNEGO unsecure basic authentication. */
    @Size(max = 10)
    public String spnegoAllowUnsecureBasic;

    /** Enable or disable SPNEGO NTLM prompt. */
    @Size(max = 10)
    public String spnegoPromptNtlm;

    /** Enable or disable SPNEGO localhost authentication. */
    @Size(max = 10)
    public String spnegoAllowLocalhost;